Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = '/input'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7f771a712048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7f77180bbb38>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None,image_width, image_height, image_channels), name = 'input_real')
    input_z    = tf.placeholder(tf.float32, (None, z_dim), name = 'input_z')
    learning_rate = tf.placeholder(tf.float32, name = 'learning_rate')
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse = reuse):
        # in 28x28x3
        x = tf.layers.conv2d(images, 64, 5, strides = 2, padding = 'same')
        x = tf.maximum(alpha * x, x) 
        # out 14x14x64
        
        x = tf.layers.conv2d(x, 128, 5, strides = 2, padding = 'same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        # out 7x7x128
        
        x = tf.layers.conv2d(x, 256, 5, strides = 2, padding = 'same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        # out 4x4x256
        
        x = tf.reshape(x, (-1, 4*4*256))
        logits = tf.layers.dense(x, 1)
        out = tf.sigmoid(logits)
        
        return out, logits
        
        

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('generator', reuse = not is_train):
        x = tf.layers.dense(z, 2*2*512)
        x = tf.reshape(x, (-1, 2, 2, 512))
        x = tf.maximum(alpha * x, x)
        # out 4x4x512
        
        x = tf.layers.conv2d_transpose(x, 256, 5, strides = 2, padding = 'valid')
        x = tf.layers.batch_normalization(x, training = is_train)
        x = tf.maximum(alpha * x, x)
        # out 8x8x256
        
        x = tf.layers.conv2d_transpose(x, 128, 5, strides = 2, padding = 'same')
        x = tf.layers.batch_normalization(x , training = is_train)
        x = tf.maximum(alpha *x, x)
        # out 16*16*128
        
        logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides = 2, padding = 'same')
        # out 32*32*3
    
        return logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    alpha = 0.2
    
    g_model = generator(input_z, out_channel_dim, is_train = True)
    d_model_real, d_logits_real = discriminator(input_real, reuse = False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse = True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real, labels = tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels = tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels = tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()
In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    n_samples, width, height, channels = data_shape
    input_real, input_z, learn_rate    = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt   = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    show_every = 50
    print_every = 10
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                batch_images *= 2
                steps += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                    
                if steps % show_every == 0:
                    n_images = 16
                    show_generator_output(sess, n_images, input_z, channels, data_image_mode)

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [13]:
batch_size = 64
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 0/2... Discriminator Loss: 0.0858... Generator Loss: 4.0506
Epoch 0/2... Discriminator Loss: 0.3248... Generator Loss: 9.2758
Epoch 0/2... Discriminator Loss: 0.3899... Generator Loss: 1.4341
Epoch 0/2... Discriminator Loss: 1.3635... Generator Loss: 10.3026
Epoch 0/2... Discriminator Loss: 0.1947... Generator Loss: 3.5274
Epoch 0/2... Discriminator Loss: 0.3242... Generator Loss: 4.2775
Epoch 0/2... Discriminator Loss: 0.2959... Generator Loss: 1.9273
Epoch 0/2... Discriminator Loss: 0.2726... Generator Loss: 2.1853
Epoch 0/2... Discriminator Loss: 0.1627... Generator Loss: 3.7094
Epoch 0/2... Discriminator Loss: 0.1847... Generator Loss: 2.3872
Epoch 0/2... Discriminator Loss: 0.1544... Generator Loss: 2.4702
Epoch 0/2... Discriminator Loss: 0.3952... Generator Loss: 1.2771
Epoch 0/2... Discriminator Loss: 0.1369... Generator Loss: 3.0052
Epoch 0/2... Discriminator Loss: 1.2104... Generator Loss: 10.1353
Epoch 0/2... Discriminator Loss: 2.1184... Generator Loss: 11.4267
Epoch 0/2... Discriminator Loss: 0.2031... Generator Loss: 4.0356
Epoch 0/2... Discriminator Loss: 0.1248... Generator Loss: 2.6039
Epoch 0/2... Discriminator Loss: 0.0793... Generator Loss: 3.4509
Epoch 0/2... Discriminator Loss: 0.0750... Generator Loss: 3.4585
Epoch 0/2... Discriminator Loss: 0.1446... Generator Loss: 2.6295
Epoch 0/2... Discriminator Loss: 0.0621... Generator Loss: 6.5722
Epoch 0/2... Discriminator Loss: 1.4573... Generator Loss: 0.4074
Epoch 0/2... Discriminator Loss: 0.1763... Generator Loss: 8.2325
Epoch 0/2... Discriminator Loss: 0.1462... Generator Loss: 2.7210
Epoch 0/2... Discriminator Loss: 0.1420... Generator Loss: 2.7468
Epoch 0/2... Discriminator Loss: 0.2356... Generator Loss: 2.0915
Epoch 0/2... Discriminator Loss: 0.4009... Generator Loss: 5.1162
Epoch 0/2... Discriminator Loss: 0.2010... Generator Loss: 3.7492
Epoch 0/2... Discriminator Loss: 0.7572... Generator Loss: 6.1542
Epoch 0/2... Discriminator Loss: 0.2190... Generator Loss: 2.2026
Epoch 0/2... Discriminator Loss: 0.6682... Generator Loss: 0.8404
Epoch 0/2... Discriminator Loss: 0.2254... Generator Loss: 2.5809
Epoch 0/2... Discriminator Loss: 0.1846... Generator Loss: 2.5759
Epoch 0/2... Discriminator Loss: 0.1351... Generator Loss: 3.2293
Epoch 0/2... Discriminator Loss: 0.1330... Generator Loss: 3.7389
Epoch 0/2... Discriminator Loss: 0.4096... Generator Loss: 8.5800
Epoch 0/2... Discriminator Loss: 0.2679... Generator Loss: 1.8524
Epoch 0/2... Discriminator Loss: 0.1501... Generator Loss: 2.6700
Epoch 0/2... Discriminator Loss: 0.1941... Generator Loss: 2.2767
Epoch 0/2... Discriminator Loss: 0.1063... Generator Loss: 3.2986
Epoch 0/2... Discriminator Loss: 0.0844... Generator Loss: 3.4062
Epoch 0/2... Discriminator Loss: 0.2190... Generator Loss: 2.3172
Epoch 0/2... Discriminator Loss: 0.1047... Generator Loss: 3.0809
Epoch 0/2... Discriminator Loss: 0.1964... Generator Loss: 2.1603
Epoch 0/2... Discriminator Loss: 0.1144... Generator Loss: 5.1357
Epoch 0/2... Discriminator Loss: 0.1373... Generator Loss: 2.5698
Epoch 0/2... Discriminator Loss: 0.0397... Generator Loss: 4.8389
Epoch 0/2... Discriminator Loss: 0.0949... Generator Loss: 3.0545
Epoch 0/2... Discriminator Loss: 0.0677... Generator Loss: 4.8360
Epoch 0/2... Discriminator Loss: 0.0622... Generator Loss: 3.5790
Epoch 0/2... Discriminator Loss: 0.1057... Generator Loss: 2.7516
Epoch 0/2... Discriminator Loss: 0.1621... Generator Loss: 2.7053
Epoch 0/2... Discriminator Loss: 0.0481... Generator Loss: 6.1646
Epoch 0/2... Discriminator Loss: 0.0404... Generator Loss: 5.1796
Epoch 0/2... Discriminator Loss: 0.0518... Generator Loss: 4.1377
Epoch 0/2... Discriminator Loss: 0.2052... Generator Loss: 2.1322
Epoch 0/2... Discriminator Loss: 0.0135... Generator Loss: 5.5178
Epoch 0/2... Discriminator Loss: 0.0516... Generator Loss: 3.7022
Epoch 0/2... Discriminator Loss: 0.0565... Generator Loss: 4.1116
Epoch 0/2... Discriminator Loss: 0.0716... Generator Loss: 3.3617
Epoch 0/2... Discriminator Loss: 0.0553... Generator Loss: 3.8810
Epoch 0/2... Discriminator Loss: 0.0444... Generator Loss: 4.0994
Epoch 0/2... Discriminator Loss: 0.0466... Generator Loss: 4.3950
Epoch 0/2... Discriminator Loss: 0.0954... Generator Loss: 3.1923
Epoch 0/2... Discriminator Loss: 0.0454... Generator Loss: 5.1396
Epoch 0/2... Discriminator Loss: 0.0715... Generator Loss: 5.2065
Epoch 0/2... Discriminator Loss: 0.0848... Generator Loss: 3.0130
Epoch 0/2... Discriminator Loss: 0.0635... Generator Loss: 3.1427
Epoch 0/2... Discriminator Loss: 0.0123... Generator Loss: 5.1506
Epoch 0/2... Discriminator Loss: 0.0533... Generator Loss: 3.6123
Epoch 0/2... Discriminator Loss: 0.0636... Generator Loss: 3.1366
Epoch 0/2... Discriminator Loss: 3.4735... Generator Loss: 22.0014
Epoch 0/2... Discriminator Loss: 0.1947... Generator Loss: 6.8974
Epoch 0/2... Discriminator Loss: 0.1206... Generator Loss: 3.4491
Epoch 0/2... Discriminator Loss: 0.2354... Generator Loss: 1.9734
Epoch 0/2... Discriminator Loss: 0.1852... Generator Loss: 2.5845
Epoch 0/2... Discriminator Loss: 0.1152... Generator Loss: 2.8131
Epoch 0/2... Discriminator Loss: 0.0670... Generator Loss: 3.5810
Epoch 0/2... Discriminator Loss: 0.1146... Generator Loss: 2.9143
Epoch 0/2... Discriminator Loss: 0.1631... Generator Loss: 2.2371
Epoch 0/2... Discriminator Loss: 0.0937... Generator Loss: 2.9173
Epoch 0/2... Discriminator Loss: 0.0840... Generator Loss: 2.9410
Epoch 0/2... Discriminator Loss: 0.0698... Generator Loss: 3.4527
Epoch 0/2... Discriminator Loss: 0.0185... Generator Loss: 5.2870
Epoch 0/2... Discriminator Loss: 0.0607... Generator Loss: 3.4994
Epoch 0/2... Discriminator Loss: 0.0528... Generator Loss: 3.7419
Epoch 0/2... Discriminator Loss: 0.0861... Generator Loss: 3.1065
Epoch 0/2... Discriminator Loss: 0.0397... Generator Loss: 4.0485
Epoch 0/2... Discriminator Loss: 0.0505... Generator Loss: 3.7876
Epoch 0/2... Discriminator Loss: 0.0287... Generator Loss: 4.6868
Epoch 0/2... Discriminator Loss: 4.9887... Generator Loss: 13.0459
Epoch 0/2... Discriminator Loss: 0.1584... Generator Loss: 2.7843
Epoch 0/2... Discriminator Loss: 0.1962... Generator Loss: 3.0285
Epoch 1/2... Discriminator Loss: 0.1835... Generator Loss: 2.1078
Epoch 1/2... Discriminator Loss: 0.1452... Generator Loss: 2.5369
Epoch 1/2... Discriminator Loss: 0.0660... Generator Loss: 3.6784
Epoch 1/2... Discriminator Loss: 0.0430... Generator Loss: 3.9890
Epoch 1/2... Discriminator Loss: 0.0479... Generator Loss: 3.7517
Epoch 1/2... Discriminator Loss: 0.0375... Generator Loss: 4.3220
Epoch 1/2... Discriminator Loss: 0.0471... Generator Loss: 3.7813
Epoch 1/2... Discriminator Loss: 0.0402... Generator Loss: 3.8161
Epoch 1/2... Discriminator Loss: 0.0387... Generator Loss: 4.3756
Epoch 1/2... Discriminator Loss: 0.0354... Generator Loss: 5.5919
Epoch 1/2... Discriminator Loss: 0.0420... Generator Loss: 4.1351
Epoch 1/2... Discriminator Loss: 0.1542... Generator Loss: 2.3982
Epoch 1/2... Discriminator Loss: 0.0418... Generator Loss: 3.8451
Epoch 1/2... Discriminator Loss: 0.0414... Generator Loss: 3.7413
Epoch 1/2... Discriminator Loss: 1.1798... Generator Loss: 0.8955
Epoch 1/2... Discriminator Loss: 0.5204... Generator Loss: 1.3342
Epoch 1/2... Discriminator Loss: 0.3017... Generator Loss: 2.4785
Epoch 1/2... Discriminator Loss: 0.2582... Generator Loss: 2.4634
Epoch 1/2... Discriminator Loss: 0.4783... Generator Loss: 1.4215
Epoch 1/2... Discriminator Loss: 0.4539... Generator Loss: 2.0622
Epoch 1/2... Discriminator Loss: 0.2327... Generator Loss: 2.5875
Epoch 1/2... Discriminator Loss: 0.1773... Generator Loss: 2.8503
Epoch 1/2... Discriminator Loss: 0.2097... Generator Loss: 2.4051
Epoch 1/2... Discriminator Loss: 0.0989... Generator Loss: 4.1704
Epoch 1/2... Discriminator Loss: 0.1737... Generator Loss: 3.0931
Epoch 1/2... Discriminator Loss: 0.0690... Generator Loss: 4.2016
Epoch 1/2... Discriminator Loss: 0.3067... Generator Loss: 1.5466
Epoch 1/2... Discriminator Loss: 0.0743... Generator Loss: 3.5108
Epoch 1/2... Discriminator Loss: 0.0341... Generator Loss: 4.6199
Epoch 1/2... Discriminator Loss: 0.0662... Generator Loss: 3.7432
Epoch 1/2... Discriminator Loss: 0.0656... Generator Loss: 3.8970
Epoch 1/2... Discriminator Loss: 0.0703... Generator Loss: 3.3953
Epoch 1/2... Discriminator Loss: 0.0605... Generator Loss: 3.4454
Epoch 1/2... Discriminator Loss: 0.0372... Generator Loss: 5.1463
Epoch 1/2... Discriminator Loss: 0.0650... Generator Loss: 3.2726
Epoch 1/2... Discriminator Loss: 0.0600... Generator Loss: 3.6085
Epoch 1/2... Discriminator Loss: 0.6515... Generator Loss: 1.5848
Epoch 1/2... Discriminator Loss: 0.1980... Generator Loss: 3.2929
Epoch 1/2... Discriminator Loss: 0.2903... Generator Loss: 4.6268
Epoch 1/2... Discriminator Loss: 0.3433... Generator Loss: 1.5444
Epoch 1/2... Discriminator Loss: 0.2140... Generator Loss: 2.0563
Epoch 1/2... Discriminator Loss: 0.1129... Generator Loss: 3.2543
Epoch 1/2... Discriminator Loss: 0.1252... Generator Loss: 2.8588
Epoch 1/2... Discriminator Loss: 0.0733... Generator Loss: 3.4238
Epoch 1/2... Discriminator Loss: 0.0903... Generator Loss: 3.2538
Epoch 1/2... Discriminator Loss: 0.0677... Generator Loss: 3.5330
Epoch 1/2... Discriminator Loss: 0.0750... Generator Loss: 3.4805
Epoch 1/2... Discriminator Loss: 0.0598... Generator Loss: 4.5217
Epoch 1/2... Discriminator Loss: 0.0464... Generator Loss: 3.9895
Epoch 1/2... Discriminator Loss: 0.0832... Generator Loss: 2.9655
Epoch 1/2... Discriminator Loss: 0.0577... Generator Loss: 3.6799
Epoch 1/2... Discriminator Loss: 0.0404... Generator Loss: 3.8001
Epoch 1/2... Discriminator Loss: 0.0273... Generator Loss: 4.4965
Epoch 1/2... Discriminator Loss: 0.0314... Generator Loss: 4.7251
Epoch 1/2... Discriminator Loss: 0.0865... Generator Loss: 2.8571
Epoch 1/2... Discriminator Loss: 0.0277... Generator Loss: 4.4945
Epoch 1/2... Discriminator Loss: 0.0319... Generator Loss: 4.1410
Epoch 1/2... Discriminator Loss: 0.0535... Generator Loss: 6.3008
Epoch 1/2... Discriminator Loss: 0.0438... Generator Loss: 3.9723
Epoch 1/2... Discriminator Loss: 0.0164... Generator Loss: 6.7563
Epoch 1/2... Discriminator Loss: 0.0621... Generator Loss: 3.3023
Epoch 1/2... Discriminator Loss: 0.0293... Generator Loss: 4.1295
Epoch 1/2... Discriminator Loss: 0.0437... Generator Loss: 3.9110
Epoch 1/2... Discriminator Loss: 0.0213... Generator Loss: 6.4791
Epoch 1/2... Discriminator Loss: 0.0676... Generator Loss: 7.1270
Epoch 1/2... Discriminator Loss: 0.0258... Generator Loss: 4.4951
Epoch 1/2... Discriminator Loss: 0.5375... Generator Loss: 11.1613
Epoch 1/2... Discriminator Loss: 0.3351... Generator Loss: 2.4360
Epoch 1/2... Discriminator Loss: 0.1003... Generator Loss: 4.3896
Epoch 1/2... Discriminator Loss: 0.1182... Generator Loss: 3.3888
Epoch 1/2... Discriminator Loss: 0.1102... Generator Loss: 2.9197
Epoch 1/2... Discriminator Loss: 0.1315... Generator Loss: 2.5980
Epoch 1/2... Discriminator Loss: 0.2217... Generator Loss: 1.9291
Epoch 1/2... Discriminator Loss: 0.0395... Generator Loss: 6.2325
Epoch 1/2... Discriminator Loss: 0.0995... Generator Loss: 2.7213
Epoch 1/2... Discriminator Loss: 0.0933... Generator Loss: 3.2175
Epoch 1/2... Discriminator Loss: 0.0256... Generator Loss: 4.9308
Epoch 1/2... Discriminator Loss: 0.1312... Generator Loss: 2.5400
Epoch 1/2... Discriminator Loss: 0.0419... Generator Loss: 3.8793
Epoch 1/2... Discriminator Loss: 0.0668... Generator Loss: 3.1706
Epoch 1/2... Discriminator Loss: 0.0467... Generator Loss: 3.7476
Epoch 1/2... Discriminator Loss: 0.0322... Generator Loss: 3.9898
Epoch 1/2... Discriminator Loss: 0.4741... Generator Loss: 1.7793
Epoch 1/2... Discriminator Loss: 0.2943... Generator Loss: 2.4022
Epoch 1/2... Discriminator Loss: 0.6920... Generator Loss: 5.7528
Epoch 1/2... Discriminator Loss: 0.2434... Generator Loss: 2.5217
Epoch 1/2... Discriminator Loss: 0.1567... Generator Loss: 3.6142
Epoch 1/2... Discriminator Loss: 0.1853... Generator Loss: 2.1985
Epoch 1/2... Discriminator Loss: 0.1177... Generator Loss: 2.9754
Epoch 1/2... Discriminator Loss: 0.0935... Generator Loss: 3.0722
Epoch 1/2... Discriminator Loss: 0.1298... Generator Loss: 2.6365
Epoch 1/2... Discriminator Loss: 0.0853... Generator Loss: 4.2590
Epoch 1/2... Discriminator Loss: 0.0454... Generator Loss: 4.3096
Epoch 1/2... Discriminator Loss: 0.0502... Generator Loss: 3.4771

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 64
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 0/1... Discriminator Loss: 3.4938... Generator Loss: 0.0499
Epoch 0/1... Discriminator Loss: 0.5402... Generator Loss: 1.2010
Epoch 0/1... Discriminator Loss: 0.1221... Generator Loss: 2.8764
Epoch 0/1... Discriminator Loss: 2.1622... Generator Loss: 0.1311
Epoch 0/1... Discriminator Loss: 0.8897... Generator Loss: 5.7223
Epoch 0/1... Discriminator Loss: 0.7763... Generator Loss: 1.3993
Epoch 0/1... Discriminator Loss: 0.8427... Generator Loss: 1.0772
Epoch 0/1... Discriminator Loss: 0.5514... Generator Loss: 1.6159
Epoch 0/1... Discriminator Loss: 0.9998... Generator Loss: 5.8804
Epoch 0/1... Discriminator Loss: 0.6187... Generator Loss: 1.0869
Epoch 0/1... Discriminator Loss: 0.2976... Generator Loss: 2.8399
Epoch 0/1... Discriminator Loss: 0.4134... Generator Loss: 4.9717
Epoch 0/1... Discriminator Loss: 0.7318... Generator Loss: 1.1749
Epoch 0/1... Discriminator Loss: 0.8213... Generator Loss: 6.5291
Epoch 0/1... Discriminator Loss: 0.4225... Generator Loss: 1.6166
Epoch 0/1... Discriminator Loss: 0.3660... Generator Loss: 1.9351
Epoch 0/1... Discriminator Loss: 0.2223... Generator Loss: 2.4438
Epoch 0/1... Discriminator Loss: 1.1260... Generator Loss: 7.6627
Epoch 0/1... Discriminator Loss: 0.5849... Generator Loss: 5.8180
Epoch 0/1... Discriminator Loss: 0.8909... Generator Loss: 2.1095
Epoch 0/1... Discriminator Loss: 1.6303... Generator Loss: 0.5718
Epoch 0/1... Discriminator Loss: 1.3515... Generator Loss: 0.6050
Epoch 0/1... Discriminator Loss: 1.6334... Generator Loss: 0.6788
Epoch 0/1... Discriminator Loss: 1.7782... Generator Loss: 0.3543
Epoch 0/1... Discriminator Loss: 1.4562... Generator Loss: 0.9451
Epoch 0/1... Discriminator Loss: 1.3935... Generator Loss: 0.6998
Epoch 0/1... Discriminator Loss: 1.1054... Generator Loss: 0.8309
Epoch 0/1... Discriminator Loss: 1.0930... Generator Loss: 1.2063
Epoch 0/1... Discriminator Loss: 0.9546... Generator Loss: 1.1287
Epoch 0/1... Discriminator Loss: 0.9852... Generator Loss: 1.1959
Epoch 0/1... Discriminator Loss: 0.7016... Generator Loss: 1.4874
Epoch 0/1... Discriminator Loss: 1.1556... Generator Loss: 1.0230
Epoch 0/1... Discriminator Loss: 1.2002... Generator Loss: 1.0247
Epoch 0/1... Discriminator Loss: 1.1374... Generator Loss: 1.0498
Epoch 0/1... Discriminator Loss: 1.1004... Generator Loss: 1.3662
Epoch 0/1... Discriminator Loss: 0.9479... Generator Loss: 1.3442
Epoch 0/1... Discriminator Loss: 0.8200... Generator Loss: 1.0630
Epoch 0/1... Discriminator Loss: 1.0303... Generator Loss: 1.0252
Epoch 0/1... Discriminator Loss: 0.8965... Generator Loss: 1.2801
Epoch 0/1... Discriminator Loss: 1.1342... Generator Loss: 0.8819
Epoch 0/1... Discriminator Loss: 1.0467... Generator Loss: 1.1661
Epoch 0/1... Discriminator Loss: 1.1546... Generator Loss: 1.7117
Epoch 0/1... Discriminator Loss: 1.4050... Generator Loss: 1.9282
Epoch 0/1... Discriminator Loss: 1.1583... Generator Loss: 0.7607
Epoch 0/1... Discriminator Loss: 1.0752... Generator Loss: 1.6591
Epoch 0/1... Discriminator Loss: 1.3561... Generator Loss: 0.7132
Epoch 0/1... Discriminator Loss: 1.1736... Generator Loss: 0.8437
Epoch 0/1... Discriminator Loss: 1.1704... Generator Loss: 0.7532
Epoch 0/1... Discriminator Loss: 1.3671... Generator Loss: 1.2006
Epoch 0/1... Discriminator Loss: 1.1223... Generator Loss: 0.9267
Epoch 0/1... Discriminator Loss: 0.8508... Generator Loss: 1.7977
Epoch 0/1... Discriminator Loss: 0.8189... Generator Loss: 1.2314
Epoch 0/1... Discriminator Loss: 0.7085... Generator Loss: 1.3500
Epoch 0/1... Discriminator Loss: 1.1879... Generator Loss: 0.6446
Epoch 0/1... Discriminator Loss: 1.0148... Generator Loss: 0.8961
Epoch 0/1... Discriminator Loss: 0.7479... Generator Loss: 1.0914
Epoch 0/1... Discriminator Loss: 0.9744... Generator Loss: 1.3654
Epoch 0/1... Discriminator Loss: 1.1665... Generator Loss: 1.8469
Epoch 0/1... Discriminator Loss: 1.1061... Generator Loss: 1.2171
Epoch 0/1... Discriminator Loss: 0.8083... Generator Loss: 1.2201
Epoch 0/1... Discriminator Loss: 1.1324... Generator Loss: 0.7556
Epoch 0/1... Discriminator Loss: 0.8404... Generator Loss: 1.6499
Epoch 0/1... Discriminator Loss: 1.3196... Generator Loss: 0.5100
Epoch 0/1... Discriminator Loss: 1.7904... Generator Loss: 0.2634
Epoch 0/1... Discriminator Loss: 1.4765... Generator Loss: 0.4011
Epoch 0/1... Discriminator Loss: 1.1267... Generator Loss: 2.1402
Epoch 0/1... Discriminator Loss: 0.9083... Generator Loss: 0.8069
Epoch 0/1... Discriminator Loss: 0.9240... Generator Loss: 2.1232
Epoch 0/1... Discriminator Loss: 0.8903... Generator Loss: 1.0455
Epoch 0/1... Discriminator Loss: 0.9617... Generator Loss: 0.8095
Epoch 0/1... Discriminator Loss: 0.7484... Generator Loss: 1.7869
Epoch 0/1... Discriminator Loss: 0.9538... Generator Loss: 1.8948
Epoch 0/1... Discriminator Loss: 0.7532... Generator Loss: 1.4906
Epoch 0/1... Discriminator Loss: 0.9748... Generator Loss: 0.8601
Epoch 0/1... Discriminator Loss: 0.6421... Generator Loss: 1.8357
Epoch 0/1... Discriminator Loss: 0.7796... Generator Loss: 1.0519
Epoch 0/1... Discriminator Loss: 0.6359... Generator Loss: 1.5979
Epoch 0/1... Discriminator Loss: 1.0251... Generator Loss: 0.7609
Epoch 0/1... Discriminator Loss: 0.5830... Generator Loss: 1.9903
Epoch 0/1... Discriminator Loss: 0.9161... Generator Loss: 1.6062
Epoch 0/1... Discriminator Loss: 1.0520... Generator Loss: 0.7133
Epoch 0/1... Discriminator Loss: 0.9087... Generator Loss: 1.4087
Epoch 0/1... Discriminator Loss: 0.7895... Generator Loss: 1.7626
Epoch 0/1... Discriminator Loss: 0.7478... Generator Loss: 1.7025
Epoch 0/1... Discriminator Loss: 1.1140... Generator Loss: 0.6647
Epoch 0/1... Discriminator Loss: 0.9956... Generator Loss: 0.8055
Epoch 0/1... Discriminator Loss: 1.1975... Generator Loss: 0.5436
Epoch 0/1... Discriminator Loss: 1.2774... Generator Loss: 0.5100
Epoch 0/1... Discriminator Loss: 0.9151... Generator Loss: 0.9271
Epoch 0/1... Discriminator Loss: 0.7162... Generator Loss: 1.0441
Epoch 0/1... Discriminator Loss: 1.1938... Generator Loss: 0.5572
Epoch 0/1... Discriminator Loss: 0.9773... Generator Loss: 0.7227
Epoch 0/1... Discriminator Loss: 0.9730... Generator Loss: 1.6589
Epoch 0/1... Discriminator Loss: 1.0297... Generator Loss: 0.7787
Epoch 0/1... Discriminator Loss: 1.0056... Generator Loss: 0.7474
Epoch 0/1... Discriminator Loss: 0.7494... Generator Loss: 1.3887
Epoch 0/1... Discriminator Loss: 1.2155... Generator Loss: 0.4521
Epoch 0/1... Discriminator Loss: 1.5023... Generator Loss: 0.3516
Epoch 0/1... Discriminator Loss: 1.7447... Generator Loss: 0.2719
Epoch 0/1... Discriminator Loss: 0.6913... Generator Loss: 1.3646
Epoch 0/1... Discriminator Loss: 1.1653... Generator Loss: 1.2579
Epoch 0/1... Discriminator Loss: 0.7750... Generator Loss: 1.2304
Epoch 0/1... Discriminator Loss: 0.6579... Generator Loss: 1.1555
Epoch 0/1... Discriminator Loss: 2.0950... Generator Loss: 0.1716
Epoch 0/1... Discriminator Loss: 0.6700... Generator Loss: 1.1103
Epoch 0/1... Discriminator Loss: 0.7221... Generator Loss: 1.3591
Epoch 0/1... Discriminator Loss: 0.8171... Generator Loss: 0.9418
Epoch 0/1... Discriminator Loss: 1.4646... Generator Loss: 0.3621
Epoch 0/1... Discriminator Loss: 0.8401... Generator Loss: 1.5850
Epoch 0/1... Discriminator Loss: 0.8072... Generator Loss: 0.9683
Epoch 0/1... Discriminator Loss: 0.7978... Generator Loss: 1.0532
Epoch 0/1... Discriminator Loss: 1.0008... Generator Loss: 1.3086
Epoch 0/1... Discriminator Loss: 1.1580... Generator Loss: 0.5276
Epoch 0/1... Discriminator Loss: 0.7423... Generator Loss: 1.3614
Epoch 0/1... Discriminator Loss: 0.8248... Generator Loss: 2.2425
Epoch 0/1... Discriminator Loss: 1.0791... Generator Loss: 1.2559
Epoch 0/1... Discriminator Loss: 0.9898... Generator Loss: 0.9625
Epoch 0/1... Discriminator Loss: 0.8957... Generator Loss: 1.3686
Epoch 0/1... Discriminator Loss: 1.2617... Generator Loss: 2.6936
Epoch 0/1... Discriminator Loss: 1.1801... Generator Loss: 1.7305
Epoch 0/1... Discriminator Loss: 0.9385... Generator Loss: 1.2423
Epoch 0/1... Discriminator Loss: 1.1758... Generator Loss: 0.6026
Epoch 0/1... Discriminator Loss: 1.1086... Generator Loss: 3.1341
Epoch 0/1... Discriminator Loss: 1.2679... Generator Loss: 0.4873
Epoch 0/1... Discriminator Loss: 1.2614... Generator Loss: 1.0156
Epoch 0/1... Discriminator Loss: 0.5078... Generator Loss: 1.9842
Epoch 0/1... Discriminator Loss: 0.7425... Generator Loss: 1.3549
Epoch 0/1... Discriminator Loss: 0.5812... Generator Loss: 1.9105
Epoch 0/1... Discriminator Loss: 0.8834... Generator Loss: 0.8106
Epoch 0/1... Discriminator Loss: 0.7389... Generator Loss: 2.2317
Epoch 0/1... Discriminator Loss: 0.7803... Generator Loss: 0.7708
Epoch 0/1... Discriminator Loss: 0.7052... Generator Loss: 1.3074
Epoch 0/1... Discriminator Loss: 1.0037... Generator Loss: 0.7350
Epoch 0/1... Discriminator Loss: 0.8991... Generator Loss: 0.7289
Epoch 0/1... Discriminator Loss: 0.9822... Generator Loss: 0.7645
Epoch 0/1... Discriminator Loss: 1.2871... Generator Loss: 0.4300
Epoch 0/1... Discriminator Loss: 0.8550... Generator Loss: 0.7997
Epoch 0/1... Discriminator Loss: 0.8084... Generator Loss: 1.5235
Epoch 0/1... Discriminator Loss: 0.9197... Generator Loss: 0.7845
Epoch 0/1... Discriminator Loss: 1.0304... Generator Loss: 0.6969
Epoch 0/1... Discriminator Loss: 0.8708... Generator Loss: 0.9413
Epoch 0/1... Discriminator Loss: 0.8471... Generator Loss: 1.1059
Epoch 0/1... Discriminator Loss: 0.9722... Generator Loss: 0.8417
Epoch 0/1... Discriminator Loss: 0.7204... Generator Loss: 1.1823
Epoch 0/1... Discriminator Loss: 0.9928... Generator Loss: 0.7287
Epoch 0/1... Discriminator Loss: 0.5861... Generator Loss: 1.8981
Epoch 0/1... Discriminator Loss: 1.0190... Generator Loss: 0.7152
Epoch 0/1... Discriminator Loss: 0.9548... Generator Loss: 2.1403
Epoch 0/1... Discriminator Loss: 0.8430... Generator Loss: 1.4128
Epoch 0/1... Discriminator Loss: 0.9684... Generator Loss: 0.8036
Epoch 0/1... Discriminator Loss: 0.8237... Generator Loss: 0.9569
Epoch 0/1... Discriminator Loss: 0.6530... Generator Loss: 1.1352
Epoch 0/1... Discriminator Loss: 1.1789... Generator Loss: 0.5395
Epoch 0/1... Discriminator Loss: 0.7321... Generator Loss: 1.0422
Epoch 0/1... Discriminator Loss: 0.9632... Generator Loss: 0.7529
Epoch 0/1... Discriminator Loss: 0.4398... Generator Loss: 1.5180
Epoch 0/1... Discriminator Loss: 0.9183... Generator Loss: 1.3262
Epoch 0/1... Discriminator Loss: 0.5432... Generator Loss: 1.3969
Epoch 0/1... Discriminator Loss: 0.9130... Generator Loss: 0.8100
Epoch 0/1... Discriminator Loss: 0.8951... Generator Loss: 0.7598
Epoch 0/1... Discriminator Loss: 0.8317... Generator Loss: 1.4023
Epoch 0/1... Discriminator Loss: 0.8042... Generator Loss: 1.3988
Epoch 0/1... Discriminator Loss: 1.0432... Generator Loss: 1.2060
Epoch 0/1... Discriminator Loss: 0.9212... Generator Loss: 0.7776
Epoch 0/1... Discriminator Loss: 0.9207... Generator Loss: 1.7040
Epoch 0/1... Discriminator Loss: 0.8457... Generator Loss: 0.9641
Epoch 0/1... Discriminator Loss: 1.0096... Generator Loss: 0.6258
Epoch 0/1... Discriminator Loss: 0.7292... Generator Loss: 0.9697
Epoch 0/1... Discriminator Loss: 1.0559... Generator Loss: 0.8529
Epoch 0/1... Discriminator Loss: 0.8762... Generator Loss: 0.7499
Epoch 0/1... Discriminator Loss: 0.9659... Generator Loss: 0.6793
Epoch 0/1... Discriminator Loss: 0.7102... Generator Loss: 1.1120
Epoch 0/1... Discriminator Loss: 1.2060... Generator Loss: 2.1606
Epoch 0/1... Discriminator Loss: 0.5394... Generator Loss: 1.5490
Epoch 0/1... Discriminator Loss: 0.7976... Generator Loss: 1.2099
Epoch 0/1... Discriminator Loss: 0.8348... Generator Loss: 1.4003
Epoch 0/1... Discriminator Loss: 0.6463... Generator Loss: 1.7573
Epoch 0/1... Discriminator Loss: 0.8142... Generator Loss: 0.8673
Epoch 0/1... Discriminator Loss: 1.8059... Generator Loss: 0.2510
Epoch 0/1... Discriminator Loss: 1.7851... Generator Loss: 3.7319
Epoch 0/1... Discriminator Loss: 0.8174... Generator Loss: 0.9701
Epoch 0/1... Discriminator Loss: 1.2161... Generator Loss: 2.8522
Epoch 0/1... Discriminator Loss: 1.0147... Generator Loss: 0.7656
Epoch 0/1... Discriminator Loss: 1.2312... Generator Loss: 1.0040
Epoch 0/1... Discriminator Loss: 0.8035... Generator Loss: 1.0998
Epoch 0/1... Discriminator Loss: 0.8026... Generator Loss: 1.1580
Epoch 0/1... Discriminator Loss: 0.6776... Generator Loss: 1.3237
Epoch 0/1... Discriminator Loss: 0.8530... Generator Loss: 1.2924
Epoch 0/1... Discriminator Loss: 0.7200... Generator Loss: 1.4268
Epoch 0/1... Discriminator Loss: 1.0659... Generator Loss: 0.6462
Epoch 0/1... Discriminator Loss: 0.5865... Generator Loss: 1.9613
Epoch 0/1... Discriminator Loss: 0.9526... Generator Loss: 0.7847
Epoch 0/1... Discriminator Loss: 0.6168... Generator Loss: 1.2453
Epoch 0/1... Discriminator Loss: 0.7432... Generator Loss: 1.1990
Epoch 0/1... Discriminator Loss: 0.6936... Generator Loss: 1.8286
Epoch 0/1... Discriminator Loss: 0.8035... Generator Loss: 1.0460
Epoch 0/1... Discriminator Loss: 0.9375... Generator Loss: 0.7069
Epoch 0/1... Discriminator Loss: 0.7581... Generator Loss: 1.1634
Epoch 0/1... Discriminator Loss: 1.1484... Generator Loss: 2.6807
Epoch 0/1... Discriminator Loss: 1.0696... Generator Loss: 0.6518
Epoch 0/1... Discriminator Loss: 1.0413... Generator Loss: 0.7719
Epoch 0/1... Discriminator Loss: 0.8431... Generator Loss: 1.1595
Epoch 0/1... Discriminator Loss: 0.6937... Generator Loss: 1.8964
Epoch 0/1... Discriminator Loss: 0.8545... Generator Loss: 0.9447
Epoch 0/1... Discriminator Loss: 0.8921... Generator Loss: 0.8225
Epoch 0/1... Discriminator Loss: 0.7641... Generator Loss: 2.1731
Epoch 0/1... Discriminator Loss: 0.5637... Generator Loss: 1.7814
Epoch 0/1... Discriminator Loss: 0.9827... Generator Loss: 1.7450
Epoch 0/1... Discriminator Loss: 1.0351... Generator Loss: 0.6126
Epoch 0/1... Discriminator Loss: 0.8312... Generator Loss: 1.6741
Epoch 0/1... Discriminator Loss: 0.9102... Generator Loss: 0.9390
Epoch 0/1... Discriminator Loss: 0.7223... Generator Loss: 1.2250
Epoch 0/1... Discriminator Loss: 1.1147... Generator Loss: 2.3964
Epoch 0/1... Discriminator Loss: 0.5496... Generator Loss: 2.0191
Epoch 0/1... Discriminator Loss: 1.5262... Generator Loss: 2.7836
Epoch 0/1... Discriminator Loss: 1.0828... Generator Loss: 0.5454
Epoch 0/1... Discriminator Loss: 0.7844... Generator Loss: 0.8652
Epoch 0/1... Discriminator Loss: 0.8597... Generator Loss: 1.2894
Epoch 0/1... Discriminator Loss: 0.9902... Generator Loss: 1.3743
Epoch 0/1... Discriminator Loss: 1.0268... Generator Loss: 0.6944
Epoch 0/1... Discriminator Loss: 1.6194... Generator Loss: 2.5139
Epoch 0/1... Discriminator Loss: 0.7370... Generator Loss: 1.3898
Epoch 0/1... Discriminator Loss: 0.7666... Generator Loss: 0.9655
Epoch 0/1... Discriminator Loss: 1.2101... Generator Loss: 2.4428
Epoch 0/1... Discriminator Loss: 1.1451... Generator Loss: 0.5843
Epoch 0/1... Discriminator Loss: 1.0694... Generator Loss: 0.5366
Epoch 0/1... Discriminator Loss: 1.3241... Generator Loss: 1.8597
Epoch 0/1... Discriminator Loss: 0.7530... Generator Loss: 1.0731
Epoch 0/1... Discriminator Loss: 0.6013... Generator Loss: 1.6352
Epoch 0/1... Discriminator Loss: 1.1545... Generator Loss: 0.7154
Epoch 0/1... Discriminator Loss: 1.1802... Generator Loss: 0.4532
Epoch 0/1... Discriminator Loss: 0.5896... Generator Loss: 1.2959
Epoch 0/1... Discriminator Loss: 1.0237... Generator Loss: 0.6844
Epoch 0/1... Discriminator Loss: 0.5687... Generator Loss: 1.9779
Epoch 0/1... Discriminator Loss: 0.7562... Generator Loss: 1.4027
Epoch 0/1... Discriminator Loss: 1.6357... Generator Loss: 0.3052
Epoch 0/1... Discriminator Loss: 0.9226... Generator Loss: 1.0681
Epoch 0/1... Discriminator Loss: 0.7895... Generator Loss: 1.2629
Epoch 0/1... Discriminator Loss: 0.8166... Generator Loss: 1.4920
Epoch 0/1... Discriminator Loss: 0.5902... Generator Loss: 1.8874
Epoch 0/1... Discriminator Loss: 0.8171... Generator Loss: 0.9425
Epoch 0/1... Discriminator Loss: 0.7412... Generator Loss: 0.9812
Epoch 0/1... Discriminator Loss: 0.8706... Generator Loss: 1.5386
Epoch 0/1... Discriminator Loss: 0.9437... Generator Loss: 0.6500
Epoch 0/1... Discriminator Loss: 1.1230... Generator Loss: 0.5199
Epoch 0/1... Discriminator Loss: 1.0221... Generator Loss: 0.7812
Epoch 0/1... Discriminator Loss: 1.6208... Generator Loss: 0.3183
Epoch 0/1... Discriminator Loss: 0.7634... Generator Loss: 1.2977
Epoch 0/1... Discriminator Loss: 1.0001... Generator Loss: 0.6887
Epoch 0/1... Discriminator Loss: 0.7739... Generator Loss: 2.0994
Epoch 0/1... Discriminator Loss: 0.8562... Generator Loss: 0.8101
Epoch 0/1... Discriminator Loss: 0.7835... Generator Loss: 0.9731
Epoch 0/1... Discriminator Loss: 0.5986... Generator Loss: 1.3333
Epoch 0/1... Discriminator Loss: 0.6061... Generator Loss: 1.4383
Epoch 0/1... Discriminator Loss: 0.8215... Generator Loss: 1.0527
Epoch 0/1... Discriminator Loss: 0.7360... Generator Loss: 0.9789
Epoch 0/1... Discriminator Loss: 1.1364... Generator Loss: 0.5063
Epoch 0/1... Discriminator Loss: 1.0235... Generator Loss: 0.6104
Epoch 0/1... Discriminator Loss: 0.8738... Generator Loss: 1.0219
Epoch 0/1... Discriminator Loss: 0.5192... Generator Loss: 1.5834
Epoch 0/1... Discriminator Loss: 0.6524... Generator Loss: 1.1100
Epoch 0/1... Discriminator Loss: 0.8013... Generator Loss: 1.6923
Epoch 0/1... Discriminator Loss: 1.2545... Generator Loss: 0.5148
Epoch 0/1... Discriminator Loss: 0.6981... Generator Loss: 2.2903
Epoch 0/1... Discriminator Loss: 0.4778... Generator Loss: 2.6618
Epoch 0/1... Discriminator Loss: 0.6691... Generator Loss: 1.8169
Epoch 0/1... Discriminator Loss: 0.5020... Generator Loss: 2.0060
Epoch 0/1... Discriminator Loss: 1.4322... Generator Loss: 0.7507
Epoch 0/1... Discriminator Loss: 0.4676... Generator Loss: 1.7071
Epoch 0/1... Discriminator Loss: 1.3114... Generator Loss: 0.4591
Epoch 0/1... Discriminator Loss: 0.8999... Generator Loss: 1.0097
Epoch 0/1... Discriminator Loss: 0.7541... Generator Loss: 0.9185
Epoch 0/1... Discriminator Loss: 0.6518... Generator Loss: 1.0822
Epoch 0/1... Discriminator Loss: 1.0121... Generator Loss: 0.5668
Epoch 0/1... Discriminator Loss: 0.7628... Generator Loss: 0.8656
Epoch 0/1... Discriminator Loss: 0.7274... Generator Loss: 0.9378
Epoch 0/1... Discriminator Loss: 1.1958... Generator Loss: 0.4737
Epoch 0/1... Discriminator Loss: 1.1576... Generator Loss: 0.5229
Epoch 0/1... Discriminator Loss: 0.7935... Generator Loss: 1.7993
Epoch 0/1... Discriminator Loss: 0.7942... Generator Loss: 1.0537
Epoch 0/1... Discriminator Loss: 0.8857... Generator Loss: 0.6911
Epoch 0/1... Discriminator Loss: 0.7224... Generator Loss: 0.9381
Epoch 0/1... Discriminator Loss: 0.6196... Generator Loss: 1.6814
Epoch 0/1... Discriminator Loss: 0.7702... Generator Loss: 0.8714
Epoch 0/1... Discriminator Loss: 0.8716... Generator Loss: 0.7652
Epoch 0/1... Discriminator Loss: 0.8137... Generator Loss: 0.8330
Epoch 0/1... Discriminator Loss: 0.8682... Generator Loss: 2.1546
Epoch 0/1... Discriminator Loss: 0.7296... Generator Loss: 1.7922
Epoch 0/1... Discriminator Loss: 0.8902... Generator Loss: 0.7831
Epoch 0/1... Discriminator Loss: 0.7700... Generator Loss: 1.2025
Epoch 0/1... Discriminator Loss: 0.8520... Generator Loss: 1.0832
Epoch 0/1... Discriminator Loss: 0.8304... Generator Loss: 0.8965
Epoch 0/1... Discriminator Loss: 0.7128... Generator Loss: 1.0559
Epoch 0/1... Discriminator Loss: 0.8692... Generator Loss: 0.7828
Epoch 0/1... Discriminator Loss: 0.9719... Generator Loss: 1.0019
Epoch 0/1... Discriminator Loss: 1.2605... Generator Loss: 0.4530
Epoch 0/1... Discriminator Loss: 0.7167... Generator Loss: 1.0940
Epoch 0/1... Discriminator Loss: 0.6705... Generator Loss: 1.3725
Epoch 0/1... Discriminator Loss: 0.6801... Generator Loss: 1.0717
Epoch 0/1... Discriminator Loss: 0.7579... Generator Loss: 1.1593
Epoch 0/1... Discriminator Loss: 1.0646... Generator Loss: 0.6110
Epoch 0/1... Discriminator Loss: 1.0849... Generator Loss: 0.5684
Epoch 0/1... Discriminator Loss: 1.2625... Generator Loss: 1.4914
Epoch 0/1... Discriminator Loss: 0.5638... Generator Loss: 1.5515
Epoch 0/1... Discriminator Loss: 0.9162... Generator Loss: 0.8087
Epoch 0/1... Discriminator Loss: 0.7913... Generator Loss: 1.1098
Epoch 0/1... Discriminator Loss: 1.7186... Generator Loss: 0.2576
Epoch 0/1... Discriminator Loss: 0.9306... Generator Loss: 1.4404
Epoch 0/1... Discriminator Loss: 0.4772... Generator Loss: 1.9560
Epoch 0/1... Discriminator Loss: 0.6171... Generator Loss: 2.1480
Epoch 0/1... Discriminator Loss: 0.7409... Generator Loss: 0.8824
Epoch 0/1... Discriminator Loss: 0.6193... Generator Loss: 1.2704
Epoch 0/1... Discriminator Loss: 0.6291... Generator Loss: 1.0322
Epoch 0/1... Discriminator Loss: 0.7296... Generator Loss: 1.1394
Epoch 0/1... Discriminator Loss: 0.8183... Generator Loss: 1.1276
Epoch 0/1... Discriminator Loss: 0.8749... Generator Loss: 0.8256

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.